Dwi Ayu Rahmawati

Institut Bisnis dan Teknologi Pelita Indonesia

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Hastuadi Harsa

National Research and Innovation Agency

Muhammad Najib Habibie

Meteorological, Climatological, And Geophysical Agency

T D Hutapea

Meteorological, Climatological, And Geophysical Agency

Andri Ramdhani

Meteorological, Climatological, And Geophysical Agency

B A Samad

Université Paris-Seine

Welly Fitria

Meteorological, Climatological, And Geophysical Agency

Alimuddin Hamzah Assegaf

Hasanuddin University

Roni Kurniawan

Universitas Pamulang

D A Suriamihardja

Hasanuddin University

Cooperative Institutions

Meteorological, Climatological, And Geophysical Agency

Hasanuddin University

National Research and Innovation Agency

National Nuclear Energy Agency of Indonesia

Indonesian Agency for Agricultural Research and Development

Indonesian National Institute of Aeronautics and Space

IPB University

Diponegoro University

Universitas Jember

Université Paris-Seine

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Hydrologic cycle of moist air in the lower atmosphere

IOP Conference Series Earth and Environmental Science (2019)

Dwi Ayu Rahmawati D A Suriamihardja Alimuddin Hamzah Assegaf B A Samad

Abstract A hydrological cycle in the atmosphere plays an important role in the Earth system. [1] The hydrological cycle in the atmosphere is a source of fresh water for life on earth. Starting from the density of heavy water, it is converted into a mild vapor density which allows an increase in water vapor from the sea surface to the dew point where the process of changing water vapor into liquid phase can take place. An atmospheric machine is represented by a modified Carnot cycle below water dome containing mixed water vapor and liquid in an atmospheric layer. This study aims to learn changes in thermodynamic parameters, such as heat and work during the moist air experiencing thermodynamic processes and the correlation of temperature and humidity to rainfall. Basic information recorded by radiosonde is used as mixed water vapor and liquid parameters representing an initial atmospheric condition in producing fresh water. The results will provide information required to figure out the process of evaporation and condensation in the troposphere and the relationship between temperature and humidity to rainfall.

Dew point

Water cycle

10.1088/1755-1315/279/1/012049

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Evaluating skill of BMKG wave model forecast (Wavewatch-3) with observation data in Indian Ocean (5 – 31 December 2017).

IOP Conference Series Earth and Environmental Science (2021)

Roni Kurniawan Hastuadi Harsa Andri Ramdhani Welly Fitria Dwi Ayu Rahmawati

Abstract Providing Maritime meteorological forecasts (including ocean wave information) is one of BMKG duties. Currently, BMKG employs Wavewatch-3 (WW3) model to forecast ocean waves in Indonesia. Evaluating the wave forecasts is very important to improve the forecasts skill. This paper presents the evaluation of 7-days ahead BMKG’s wave forecast. The evaluation was performed by comparing wave data observation and BMKG wave forecast. The observation data were obtained from RV Mirai 1708 cruise on December 5th to 31st 2017 at the Indian Ocean around 04°14'S and 101°31'E. Some statistical properties and Relative Operating Characteristics (ROC) curve were utilized to assess the model performance. The evaluation processes were carried out on model’s parameters: Significant Wave Height (Hs) and Wind surface for each 7-days forecast started from 00 UTC. The comparation results show that, in average, WW3 forecasts are over-estimate the wave height than that of the observation. The forecast skills determined from the correlation and ROC curves are good for the first- and second-day forecast, while the third until seventh day decrease to fair. This phenomenon is suspected to be caused by the wind data characteristics provided by the Global Forecasts System (GFS) as the input of the model. Nevertheless, although statistical correlation is good for up to 2 days forecast, the average value of Root Mean Square Error (RMSE), absolute bias, and relative error are high. In general, this verifies the overestimate results of the model output and should be taken into consideration to improve BMKG’s wave model performance and forecast accuracy.

Significant wave height

Wave height

Wave model

Mean absolute error

10.1088/1755-1315/893/1/012058

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